This review classifies the kinematic measures used to evaluate post-stroke motor impairment following upper limb robot-assisted rehabilitation and investigates their correlations with clinical outcome measures. An online literature search was carried out in PubMed, MEDLINE, Scopus and IEEE-Xplore databases. Kinematic parameters mentioned in the studies included were categorized into the International Classification of Functioning, Disability and Health (ICF) domains. The correlations between these parameters and the clinical scales were summarized. Forty-nine kinematic parameters were identified from 67 articles involving 1750 patients. The most frequently used parameters were: movement speed, movement accuracy, peak speed, number of speed peaks, and movement distance and duration. According to the ICF domains, 44 kinematic parameters were categorized into Body Functions and Structure, 5 into Activities and no parameters were categorized into Participation and Personal and Environmental Factors. Thirteen articles investigated the correlations between kinematic parameters and clinical outcome measures. Some kinematic measures showed a significant correlation coefficient with clinical scores, but most were weak or moderate. The proposed classification of kinematic measures into ICF domains and their correlations with clinical scales could contribute to identifying the most relevant ones for an integrated assessment of upper limb robot-assisted rehabilitation treatments following stroke. Increasing the assessment frequency by means of kinematic parameters could optimize clinical assessment procedures and enhance the effectiveness of rehabilitation treatments.

Both transcranial direct current stimulation (tDCS) and wrist robot-assisted training have demonstrated to be promising approaches for stroke rehabilitation. However, the effects of the combination of the two treatments in subacute stroke patients are not yet clear. To investigate the effectiveness of combining tDCS and wrist robot-assisted rehabilitation in subacute stroke patients in comparison with the wrist robotic training only, a single-blind, randomized, sham-controlled trial was performed with 40 subacute stroke patients (25 ± 7 days from stroke onset time). Patients were randomly assigned to experimental group (EG, n = 20) where patients receive real tDCS (2mA, 20 minutes, the anodal electrode on the primary motor cortex -M1 -area of the affected hemisphere(C3/C4 in the 10-20 EEG system and the cathodal electrode on the contralateral orbit bone) or control group (CG, n = 20) where patients receive sham tDCS (5 seconds) during wrist robotic rehabilitation training. The effects of the treatment were evaluated by means of the upper extremity, shoulder-elbow and wrist subsections of the Fugl–Meyer Assessment Scale, Modified Ashworth Scale, Motricity Index and Box & Block test together with kinematic parameters. One out of 20 patients in the CG did not complete the treatment. All the clinical outcome measures except the Modified Ashworth Scale showed a significant increase after the treatment in both groups. However, no significant difference in the average changes after treatment between groups was observed. The movement velocity and smoothness showed significant increases after the training, even though no significant difference between groups was observed. The combination of wrist robot-assisted training and tDCS did not show additional effects in comparison with wrist robot-assisted training only in subacute stroke patients. The negative results found in this study are specific for the specific intervention. The timing of delivering the tDCS and the robot-assisted therapy has to be deeply investigated to enhance the effectiveness of the training.

In this study, the recovery of proximal and distal segments in stroke patients who received distal training alone was investigated. Forty (20 subacute and 20 chronic) stroke patients were recruited to perform wrist robot-assisted rehabilitation training. The upper extremity, shoulder-elbow and wrist subsections of the Fugl-Meyer Assessment Scale were used to assess the motor recovery of distal and proximal segments. In addition, the Modified Ashworth Scale, the Motricity Index and the Box & Block test were used as clinical outcome measures together with kinematic parameters to evaluate the effects of the training. Significant increases in the wrist and shoulder-elbow subsections of the Fugl-Meyer Assessment Scale, Motricity Index and Box & Block test were found in both groups. Average changes in shoulder-elbow and upper extremity subsections of the Fugl-Meyer Assessment Scale in the subacute group (6.10 ± 6.60 and 15.65 ± 14.04) were significantly higher (p < 0.05) than those in the chronic group (2.30 ± 2.76 and 6.60 ± 4.64). In addition, significant increases in the movement velocity, movement smoothness and movement quality were observed in the subacute group. Our findings provide evidence that following a robot-assisted rehabilitation treatment there is a distal-to-proximal generalization in subacute stroke patients.

Backgrounds and Objective: Facial palsy is a complex pathophysiological condition affecting the personal and professional lives of the involved patients. Sudden muscle weakness or paralysis needs to be rehabilitated to recover a symmetric and expressive face. Computer-aided decision support systems for facial rehabilitation have been developed. However, there is a lack of facial muscle baseline data to evaluate the patient states and guide as well as optimize the rehabilitation strategy. In this present study, we aimed to develop a novel baseline facial muscle database (static and dynamic behaviors) using the coupling between statistical shape modeling and in-silico trial approaches. Methods: 10,000 virtual subjects (5000 males and 5000 females) were generated from a statistical shape modeling (SSM) head model. Skull and muscle networks were defined so that they statistically fit with the head shapes. Two standard mimics: smiling and kissing were generated. The muscle strains of the lengths in neutral and mimic positions were computed and recorded thanks to the muscle insertion and attachment points on the animated head and skull meshes. For validation, five head and skull meshes were reconstructed from the five computed tomography (CT) image sets. Skull and muscle networks were then predicted from the reconstructed head meshes. The predicted skull meshes were compared with the reconstructed skull meshes based on the mesh-to-mesh distance metrics. The predicted muscle lengths were also compared with those manually defined on the reconstructed head and skull meshes. Moreover, the computed muscle lengths and strains were compared with those in our previous studies and the literature. Results: The skull prediction’s median deviations from the CT-based models were 2.2236 mm, 2.1371 mm, and 2.1277 mm for the skull shape, skull mesh, and muscle attachment point regions, respectively. The median deviation of the muscle lengths was 4.8940 mm. The computed muscle strains were compatible with the reported values in our previous Kinect-based method and the literature. Conclusions: The development of our novel facial muscle database opens new avenues to accurately evaluate the facial muscle states of facial palsy patients. Based on the evaluated results, specific types of facial mimic rehabilitation exercises can also be selected optimally to train the target muscles. In perspective, the database of the computed muscle lengths and strains will be integrated into our available clinical decision support system for automatically detecting malfunctioning muscles and proposing patient-specific rehabilitation serious games.